{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# from tqdm import tqdm_notebook as tqdm\n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "import matplotlib\n",
    "from matplotlib import cm\n",
    "# This import registers the 3D projection, but is otherwise unused.\n",
    "from mpl_toolkits.mplot3d import Axes3D  # noqa: F401 unused import\n",
    "from matplotlib.collections import PolyCollection\n",
    "from matplotlib import colors as mcolors\n",
    "\n",
    "\n",
    "from scipy.interpolate import CubicSpline, UnivariateSpline, griddata\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "def bin_data(xi, yi):\n",
    "    x = np.unique(xi)\n",
    "    y = [None] * x.size\n",
    "    for i in range(x.size): y[i] = yi[xi == x[i]]\n",
    "    # Return \n",
    "    y_mean = np.array([a.mean() for a in y])\n",
    "    y_err = np.array([np.std(a) / (a.size**0.5) for a in y])\n",
    "    return (x, y_mean, y_err, y, xi, yi)\n",
    "\n",
    "\n",
    "\n",
    "def cc(arg):\n",
    "    '''\n",
    "    Shorthand to convert 'named' colors to rgba format at 60% opacity.\n",
    "    '''\n",
    "    return mcolors.to_rgba(arg, alpha=0.6)\n",
    "\n",
    "\n",
    "def polygon_under_graph(xlist, ylist):\n",
    "    '''\n",
    "    Construct the vertex list which defines the polygon filling the space under\n",
    "    the (xlist, ylist) line graph.  Assumes the xs are in ascending order.\n",
    "    '''\n",
    "    return [(xlist[0], 0.), *zip(xlist, ylist), (xlist[-1], 0.)]\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "colorstyle=dict(red_face = np.array([255,85,65])/255, red_edge = np.array([201,67,52])/255,\n",
    "                blue_face = np.array([49,115,255])/255, blue_edge = np.array([36,85,189])/255,\n",
    "                green_face= np.array([84,224,81])/255, green_edge= np.array([62,166,60])/255,\n",
    "                yellow_face=np.array([255,207,49])/255,yellow_edge=np.array([191,155,36])/255,\n",
    "                gray_face=np.array([169,169,169])/255,gray_edge=np.array([137,137,137])/255)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fig.2B-D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "import scipy.io as sio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "DataRead=sio.loadmat('Fig2BCD.mat',squeeze_me=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from types import SimpleNamespace \n",
    "data1 = SimpleNamespace(**DataRead)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Fig.2B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Binx=2\n",
    "Biny=2\n",
    "boxwidth=0.24\n",
    "boxspacing=0.035\n",
    "boxdisplace=boxwidth+boxspacing\n",
    "camp_temp='bwr'\n",
    "\n",
    "clim=[-2,2]\n",
    "\n",
    "fig = plt.figure(figsize=[3.,3.])\n",
    "ax1=fig.add_axes([0.12,0.05,boxwidth,boxwidth])\n",
    "ax2=fig.add_axes([0.12+boxdisplace,0.05,boxwidth,boxwidth])\n",
    "ax3=fig.add_axes([0.12+boxdisplace*2,0.05,boxwidth,boxwidth])\n",
    "\n",
    "\n",
    "\n",
    "ax1.imshow(data1.app_show1,cmap=camp_temp,clim=clim,aspect=Biny/Binx,origin='lower')\n",
    "\n",
    "ax1.tick_params(axis='x',top=False,bottom=True,length=1.5,direction='out',labelsize=9,pad=2)\n",
    "ax1.tick_params(axis='y',length=1.5,direction='out',labelsize=9,pad=2)\n",
    "\n",
    "ax1.set_xticks([])\n",
    "ax1.set_yticks([0,0+45/(2.1*Biny),0+90/(2.1*Biny)])\n",
    "ax1.set_xticks([data1.app_show1.shape[1]/2-0.5-50/(2.1*Biny),data1.app_show1.shape[1]/2-0.5,data1.app_show1.shape[1]/2+50/(2.1*Biny)-0.5])\n",
    "\n",
    "ax1.set_ylim([0,0+90/(2.1*Biny)])\n",
    "ax1.set_yticklabels(['0','45','90'])\n",
    "ax1.set_xticklabels(['-50','0','50'])\n",
    "ax1.set_ylabel(r'z ($\\mu m$)',fontsize=9,labelpad=2)\n",
    "ax1.set_xlabel(r'x ($\\mu m$)',fontsize=9,labelpad=-2)\n",
    "ax1.xaxis.set_label_position('bottom') \n",
    "ax1.set_xticks([10/(2.1*Biny),0+60/(2.1*Biny),0+110/(2.1*Biny)])\n",
    "ax1.set_xticklabels(['-50','0','50'])\n",
    "\n",
    "ax1.set_title('$t=0\\,\\mathrm{ms}$',fontsize=9,pad=2)\n",
    "\n",
    "ax2.imshow(data1.app_show2,cmap=camp_temp,clim=clim,aspect=Biny/Binx,origin='lower')\n",
    "ax2.tick_params(axis='both',length=0)\n",
    "ax2.set_xticks([])\n",
    "ax2.set_yticks([])\n",
    "ax2.set_ylim([0,0+90/(2.1*Biny)])\n",
    "# ax2.set_xlabel(r'x',fontsize=9,labelpad=2)\n",
    "ax2.xaxis.set_label_position('bottom') \n",
    "ax2.set_title('$t=26\\,\\mathrm{ms}$',fontsize=9,pad=2)\n",
    "\n",
    "ax3.imshow(data1.app_show3,cmap=camp_temp,clim=clim,aspect=Biny/Binx,origin='lower')\n",
    "ax3.tick_params(axis='x',length=0)\n",
    "ax3.set_xticks([])\n",
    "ax3.set_yticks([])\n",
    "ax3.set_ylim([0,0+90/(2.1*Biny)])\n",
    "# ax3.set_xlabel(r'x',fontsize=9,labelpad=2)\n",
    "ax3.xaxis.set_label_position('bottom') \n",
    "ax3.set_title('$t=54\\,\\mathrm{ms}$',fontsize=9,pad=2)\n",
    "\n",
    "\n",
    "fig.savefig('Fig2A.pdf',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_DT_t0=pd.DataFrame(data1.app_show1)\n",
    "# df_DT_t0.to_csv('Fig2B_t0.csv',index=False,header=False)\n",
    "\n",
    "# df_DT_t26=pd.DataFrame(data1.app_show2)\n",
    "# df_DT_t26.to_csv('Fig2B_t26.csv',index=False,header=False)\n",
    "\n",
    "# df_DT_t54=pd.DataFrame(data1.app_show3)\n",
    "# df_DT_t54.to_csv('Fig2B_t54.csv',index=False,header=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Fig.2CD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/n9/rcvpsjbd7mgdmnxtthsmf5400000gn/T/ipykernel_57144/2835173446.py:47: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  axs[1].set_yticklabels(['-4','','0','','4'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxwidth=0.71\n",
    "boxheight=0.3\n",
    "boxspacing=0.03\n",
    "boxleftspace=0.17\n",
    "\n",
    "cb_width=0.015\n",
    "cb_spacing=0.01\n",
    "\n",
    "dn_rf_err=0.0016\n",
    "dT_err=dn_rf_err/2.45*500\n",
    "\n",
    "fig,axs=plt.subplots(figsize=[3,3],nrows=2,ncols=1,sharex=True)\n",
    "\n",
    "plt.sca(axs[0])\n",
    "# plt.title(r'$\\Delta T$ (nK)',fontsize=10)\n",
    "axs[0].set_position([boxleftspace,0.15+boxheight+boxspacing,boxwidth,boxheight])\n",
    "axs[0].tick_params(axis='both',direction='out',top=0,right=0,bottom=0,labelsize=9,length=1.5)\n",
    "# plt.pcolor(movie_T[0],movie_T[1],movie_T[2]*TF, cmap='bwr',linewidth=0)\n",
    "plt.pcolor(data1.time,data1.z,data1.dT2D, cmap='bwr',linewidth=0)\n",
    "plt.clim([-5,5])\n",
    "plt.ylim([0,90])\n",
    "plt.yticks([0,45,90])\n",
    "plt.ylabel('$z$ ($\\mu$m)',fontsize=9,position=(0,0.5))\n",
    "# axs[0].set_yticks([])\n",
    "ax_cb=fig.add_axes([boxleftspace+boxwidth+cb_spacing,0.15+boxheight+boxspacing,cb_width,boxheight])\n",
    "ax_cb.tick_params(axis='both',direction='in',right=1,labelsize=9,left=0,length=1.5,pad=1)\n",
    "cb=plt.colorbar(cax=ax_cb,orientation='vertical')\n",
    "cb.set_ticks([-5,5])\n",
    "ax_cb.set_ylabel('$\\Delta T$ (nK)',fontsize=9,labelpad=-9)\n",
    "\n",
    "plt.sca(axs[1])\n",
    "\n",
    "# plt.plot(data1.time, data1.dT_Fourier,'o',color=colorstyle['blue_edge'],\n",
    "#          markeredgecolor=colorstyle['blue_edge'],markerfacecolor=colorstyle['blue_face'],\n",
    "#         markersize=1.5,mew=0.5)\n",
    "\n",
    "plt.errorbar(data1.time, data1.dT_Fourier,yerr=dT_err,marker='o',color='k',\n",
    "         markeredgecolor='k',markerfacecolor=colorstyle['gray_edge'],\n",
    "        markersize=2.5,mew=0.5,linestyle='None',elinewidth=0.3)\n",
    "\n",
    "plt.plot(data1.x_fit,data1.y_fit,linewidth=1,color='k',linestyle='--')\n",
    "\n",
    "plt.xlim([0,200])\n",
    "axs[1].tick_params(axis='both',direction='out',top=0,right=0,labelsize=9,length=1.5)\n",
    "axs[1].set_position([boxleftspace,0.15,boxwidth,boxheight])\n",
    "axs[1].set_xlabel('t (ms)',fontsize=9,labelpad=1)\n",
    "axs[1].set_yticklabels(['-4','','0','','4'])\n",
    "# axs[1].set_yticks([])\n",
    "# plt.ylabel(r'$\\tilde{\\Delta} T$ (nK)',fontsize=7,position=(0,0.5))\n",
    "plt.ylabel(r'${\\Delta T}$ (nK)',fontsize=9,position=(0,0.5))\n",
    "\n",
    "# plt.ylabel(r'$\\mathcal{F}\\ [{\\Delta} T]$ (nK)',fontsize=7,position=(0,0.5))\n",
    "plt.yticks([-4,-2,0,2,4])\n",
    "plt.ylim([-5,5])\n",
    "\n",
    "fig.savefig('Fig2BC.pdf',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_DT_z_t=pd.DataFrame(data1.dT2D)\n",
    "# df_DT_z_t.to_csv('Fig2C.csv',index=False,header=False)\n",
    "# df_DT_z_t_z=pd.DataFrame(data1.z)\n",
    "# df_DT_z_t_z.to_csv('Fig2C_z.csv',index=False,header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_DT_k1_t=pd.DataFrame({'t':data1.time,'dT':data1.dT_Fourier,'dT_err':dT_err})\n",
    "# df_DT_k1_t.to_csv('Fig2D.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fig2FG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all=pd.read_pickle('Fig2FG.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all['RF_evolutionNN']=[None]*len(df_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fitfun_decay(t,nu=20,A=1,B=0,gamma=1,C=0):\n",
    "    return np.exp(-gamma*2*np.pi*t/1000/2)*(A*np.cos(np.sqrt(nu**2-(gamma/2)**2)*2*np.pi*t/1000)+B*np.sin(np.sqrt(nu**2-(gamma/2)**2)*2*np.pi*t/1000))+C\n",
    "\n",
    "def fitfun_overdamp(t,nu=20,A=1,B=0,gamma=1,C=0):\n",
    "    return np.exp(-gamma*2*np.pi*t/1000/2)*(A*np.exp(-np.sqrt((gamma/2)**2-nu**2)*2*np.pi*t/1000)+B*np.exp(np.sqrt((gamma/2)**2-nu**2)*2*np.pi*t/1000))+C\n",
    "\n",
    "def fitfun_expdecay(t,alpha=20,A=1,offset=0):\n",
    "    return A*np.exp(-alpha*2*np.pi*t/1000)+offset\n",
    "\n",
    "index=[0,2,4,6,9,14]\n",
    "RF_space=-1.6\n",
    "Tc=0.167\n",
    "\n",
    "xfit_show=np.linspace(0,400,2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/n9/rcvpsjbd7mgdmnxtthsmf5400000gn/T/ipykernel_57144/520120266.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_all.RF_evolutionNN[i]=(df_all.RF_evolutionN[i]-offset_temp)/amp_temp\n"
     ]
    }
   ],
   "source": [
    "for i,r in df_all.iterrows():\n",
    "    if df_all.Fit_method[i]=='Sinusoidal':\n",
    "        offset_temp=df_all.popt_sin[i][-1]\n",
    "        amp_temp=fitfun_decay(xfit_show,*df_all.popt_sin[i][:-1])[0]-offset_temp\n",
    "    elif df_all.Fit_method[i]=='Overdampen':\n",
    "        offset_temp=df_all.popt_overdamp[i][-1]\n",
    "        amp_temp=fitfun_overdamp(xfit_show,*df_all.popt_overdamp[i][:-1])[0]-offset_temp\n",
    "    else:\n",
    "        offset_temp=df_all.popt_exp[i][-1]\n",
    "        amp_temp=fitfun_expdecay(xfit_show,*df_all.popt_exp[i][:-1])[0]-offset_temp\n",
    "        \n",
    "    df_all.RF_evolutionNN[i]=(df_all.RF_evolutionN[i]-offset_temp)/amp_temp\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig,ax=plt.subplots(figsize=[2.25,3.3],nrows=1,ncols=1)\n",
    "\n",
    "# ax.set_position([0.12,0.14,0.72,0.85])\n",
    "\n",
    "# for j in range(len(index)):\n",
    "#     i=index[j]\n",
    "#     r=df_all.iloc[i]\n",
    "    \n",
    "    \n",
    "#     temperature='{:.2f}$T_c$'.format(r.TTilde/Tc)\n",
    "#     temp_text_string=temperature\n",
    "#     plt.text(140,-RF_space*j+0.33,temp_text_string,fontsize=9)\n",
    "#     offset_temp=0\n",
    "#     if df_all.Fit_method[i]=='Sinusoidal':\n",
    "#         plt.plot(xfit_show,fitfun_decay(xfit_show,*df_all.popt_sin[i][:-1])-RF_space*j,'--',color='k',alpha=0.9,linewidth=0.75)\n",
    "#         offset_temp=df_all.popt_sin[i][-1]\n",
    "#     elif df_all.Fit_method[i]=='Overdampen':\n",
    "#         plt.plot(xfit_show,fitfun_overdamp(xfit_show,*df_all.popt_overdamp[i][:-1])-RF_space*j,'--',color='k',alpha=0.9,linewidth=0.75)\n",
    "#         offset_temp=df_all.popt_overdamp[i][-1]\n",
    "#     else:\n",
    "#         plt.plot(xfit_show,fitfun_expdecay(xfit_show,*df_all.popt_exp[i][:-1])-RF_space*j,'--',color='k',alpha=0.9,linewidth=0.75)\n",
    "#         offset_temp=df_all.popt_exp[i][-1]\n",
    "        \n",
    "# #     plt.plot(df_all.WaitTime[i],df_all.RF_evolutionN[i]-offset_temp-RF_space*j,'o',markersize=1.5,mew=0.5,mfc=zs.constants.colorstyle['blue_face'],mec=zs.constants.colorstyle['blue_edge'])\n",
    "    \n",
    "    \n",
    "#     plt.errorbar(df_all.WaitTime[i],df_all.RF_evolutionN[i]-offset_temp-RF_space*j,yerr=df_all.rf_errN[i],\n",
    "#                  marker='o',linestyle='None',elinewidth=0.3,markersize=1.5,mew=0.5,mfc=zs.constants.colorstyle['blue_face'],mec=zs.constants.colorstyle['blue_edge'],ecolor=zs.constants.colorstyle['blue_edge'])\n",
    "    \n",
    "\n",
    "    \n",
    "    \n",
    "#     plt.axhline(-RF_space*j,linestyle=':',color=[0.3,0.3,0.3],alpha=0.4,linewidth=0.5)\n",
    "    \n",
    "#     temperature='T/Tc={:.3f}'.format(r.TTilde/Tc)\n",
    "#     Condensate='Cond={:.3f}'.format(r.Cond)\n",
    "#     temp_text_string=r.DS_name+', '+temperature+', '+Condensate\n",
    "#     plt.xlim([0,200])\n",
    "    \n",
    "    \n",
    "# plt.ylim([RF_space*0.6,-RF_space*(len(index)-0.2)])\n",
    "\n",
    "# plt.ylabel('$\\Delta T$ (a.u.)',fontsize=9,labelpad=-2)\n",
    "# ax.tick_params(axis='both',direction='out',left=0,top=0,right=0,labelsize=9,length=1.5)\n",
    "# ax.set_xlabel('t (ms)',fontsize=9,labelpad=1)\n",
    "# plt.yticks(np.linspace(-(len(index)-1)*RF_space,0,len(index)))\n",
    "# ax.set_yticklabels(['','','','','',''])\n",
    "\n",
    "# fig.savefig('LocalHeater_Waterfall.pdf',dpi=300)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 225x330 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(figsize=[2.25,3.3],nrows=1,ncols=1)\n",
    "\n",
    "ax.set_position([0.12,0.14,0.72,0.85])\n",
    "\n",
    "for j in range(len(index)):\n",
    "    i=index[j]\n",
    "    r=df_all.iloc[i]\n",
    "    \n",
    "    \n",
    "    temperature='{:.2f}$T_c$'.format(r.TTilde/Tc)\n",
    "    temp_text_string=temperature\n",
    "    plt.text(140,-RF_space*j+0.33,temp_text_string,fontsize=9)\n",
    "    offset_temp=0\n",
    "    if df_all.Fit_method[i]=='Sinusoidal':\n",
    "        plt.plot(xfit_show,fitfun_decay(xfit_show,*df_all.popt_sin[i][:-1])-RF_space*j,'--',color='k',alpha=0.9,linewidth=0.75)\n",
    "        offset_temp=df_all.popt_sin[i][-1]\n",
    "    elif df_all.Fit_method[i]=='Overdampen':\n",
    "        plt.plot(xfit_show,fitfun_overdamp(xfit_show,*df_all.popt_overdamp[i][:-1])-RF_space*j,'--',color='k',alpha=0.9,linewidth=0.75)\n",
    "        offset_temp=df_all.popt_overdamp[i][-1]\n",
    "    else:\n",
    "        plt.plot(xfit_show,fitfun_expdecay(xfit_show,*df_all.popt_exp[i][:-1])-RF_space*j,'--',color='k',alpha=0.9,linewidth=0.75)\n",
    "        offset_temp=df_all.popt_exp[i][-1]\n",
    "        \n",
    "#     plt.plot(df_all.WaitTime[i],df_all.RF_evolutionN[i]-offset_temp-RF_space*j,'o',markersize=1.5,mew=0.5,mfc=colorstyle['blue_face'],mec=colorstyle['blue_edge'])\n",
    "    \n",
    "    \n",
    "    plt.errorbar(df_all.WaitTime[i],df_all.RF_evolutionN[i]-offset_temp-RF_space*j,yerr=df_all.rf_errN[i],\n",
    "                 marker='o',linestyle='None',elinewidth=0.5,markersize=2.5,mew=0.5,mfc=colorstyle['gray_edge'],mec='k',ecolor='k')\n",
    "    \n",
    "\n",
    "    \n",
    "    \n",
    "    plt.axhline(-RF_space*j,linestyle=':',color=[0.3,0.3,0.3],alpha=0.4,linewidth=0.5)\n",
    "    \n",
    "    temperature='T/Tc={:.3f}'.format(r.TTilde/Tc)\n",
    "    Condensate='Cond={:.3f}'.format(r.Cond)\n",
    "    temp_text_string=r.DS_name+', '+temperature+', '+Condensate\n",
    "    plt.xlim([0,200])\n",
    "    \n",
    "    \n",
    "plt.ylim([RF_space*0.6,-RF_space*(len(index)-0.2)])\n",
    "\n",
    "plt.ylabel('$\\Delta T$ (a.u.)',fontsize=9,labelpad=-2)\n",
    "ax.tick_params(axis='both',direction='out',left=0,top=0,right=0,labelsize=9,length=1.5)\n",
    "ax.set_xlabel('t (ms)',fontsize=9,labelpad=1)\n",
    "plt.yticks(np.linspace(-(len(index)-1)*RF_space,0,len(index)))\n",
    "ax.set_yticklabels(['','','','','',''])\n",
    "\n",
    "fig.savefig('Fig2D.pdf',dpi=300)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# i=14\n",
    "# print('T/Tc={:.2f}'.format(df_all.TTilde[i]/Tc))\n",
    "\n",
    "# pd.DataFrame(df_all.WaitTime[i]).to_clipboard(index=False,header=False)\n",
    "# pd.DataFrame(df_all.RF_evolutionN[i]).to_clipboard(index=False,header=False)\n",
    "# df_all.rf_errN[i]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## colormap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import colors as mcolors\n",
    "colorsraw = [\n",
    "    (10, 49, 244),\n",
    "#     (130,75,111),\n",
    "    (252, 30, 32)\n",
    "]\n",
    "colors = [[c/255 for c in cl] for cl in colorsraw]\n",
    "\n",
    "cmapbuild = mcolors.LinearSegmentedColormap.from_list('colormap', colors)\n",
    "\n",
    "def cmapscaled2(temp, cscale, alpha, saturation=0.7, lightness=0.9):\n",
    "#     return [*cmapbuild(temp/cscale)[:2],alpha]\n",
    "    temp=temp-.07\n",
    "    originalhsv = np.array(mcolors.rgb_to_hsv(cmapbuild(temp/cscale)[:3]))\n",
    "    modifier = np.array([0,saturation,lightness])\n",
    "    newrgb = mcolors.hsv_to_rgb(originalhsv-modifier)\n",
    "    return [*newrgb[:3], alpha]\n",
    "\n",
    "cmapscaled = cmapscaled2\n",
    "\n",
    "cscale=1\n",
    "\n",
    "fillcmap = lambda temp: cmapscaled(temp, cscale, 0.2, 0, 0.1)\n",
    "facecmap = lambda temp: cmapscaled(temp, cscale, 1, 0.4,.010)\n",
    "edgecmap = lambda temp: cmapscaled(temp, cscale, 1, 0.1,0.025)\n",
    "linecmap = lambda temp: cmapscaled(temp, cscale, 0.9,0.1,.025)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 225x330 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(figsize=[2.25,3.3],nrows=1,ncols=1)\n",
    "\n",
    "ax.set_position([0.12,0.14,0.76,0.85])\n",
    "\n",
    "for j in range(len(index)):\n",
    "    i=index[j]\n",
    "    r=df_all.iloc[i]\n",
    "    \n",
    "    \n",
    "    temperature='{:.2f}$T_c$'.format(r.TTilde/Tc)\n",
    "    temp_text_string=temperature\n",
    "    plt.text(150,-RF_space*j+0.33,temp_text_string,fontsize=8)\n",
    "    offset_temp=0\n",
    "    if df_all.Fit_method[i]=='Sinusoidal':\n",
    "        plt.plot(xfit_show,fitfun_decay(xfit_show,*df_all.popt_sin[i][:-1])-RF_space*j,'--',color=linecmap(j/(len(index)-1)),alpha=0.7,linewidth=0.75)\n",
    "        offset_temp=df_all.popt_sin[i][-1]\n",
    "    elif df_all.Fit_method[i]=='Overdampen':\n",
    "        plt.plot(xfit_show,fitfun_overdamp(xfit_show,*df_all.popt_overdamp[i][:-1])-RF_space*j,'--',color=linecmap(j/(len(index)-1)),alpha=0.7,linewidth=0.75)\n",
    "        offset_temp=df_all.popt_overdamp[i][-1]\n",
    "    else:\n",
    "        plt.plot(xfit_show,fitfun_expdecay(xfit_show,*df_all.popt_exp[i][:-1])-RF_space*j,'--',color=linecmap(j/(len(index)-1)),alpha=0.7,linewidth=0.75)\n",
    "        offset_temp=df_all.popt_exp[i][-1]\n",
    "        \n",
    "    plt.plot(df_all.WaitTime[i],df_all.RF_evolutionN[i]-offset_temp-RF_space*j,'o',markersize=1.5,mew=0.5,mfc=facecmap(j/(len(index)-1)),mec=edgecmap(j/(len(index)-1)))\n",
    "    \n",
    "    plt.axhline(-RF_space*j,linestyle=':',color=[0.3,0.3,0.3],alpha=0.4,linewidth=0.5)\n",
    "    \n",
    "    temperature='T/Tc={:.3f}'.format(r.TTilde/Tc)\n",
    "    Condensate='Cond={:.3f}'.format(r.Cond)\n",
    "    temp_text_string=r.DS_name+', '+temperature+', '+Condensate\n",
    "    plt.xlim([0,150])\n",
    "    plt.grid()\n",
    "    plt.ylabel('RF Response (a.u.)',fontsize=9,labelpad=0)\n",
    "#     plt.xlabel('Time [ms]',fontsize=9)\n",
    "\n",
    "ax.tick_params(axis='both',direction='in',length=2,top=1,right=1,labelsize=7)\n",
    "ax.set_xlabel('t (ms)',fontsize=9,labelpad=1)\n",
    "plt.yticks(np.linspace(-(len(index)-1)*RF_space,0,len(index)))\n",
    "ax.set_yticklabels(['','','','','',''])\n",
    "\n",
    "fig.savefig('LocalHeater_Waterfall_colorful.pdf',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/n9/rcvpsjbd7mgdmnxtthsmf5400000gn/T/ipykernel_57144/3499726181.py:4: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
      "  bwr_cmap = cm.get_cmap('bwr', 256)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import cm\n",
    "from matplotlib.colors import ListedColormap, LinearSegmentedColormap\n",
    "\n",
    "bwr_cmap = cm.get_cmap('bwr', 256)\n",
    "\n",
    "a=1.0\n",
    "\n",
    "sclae=a*np.linspace(0, 1, 256)+(1-a)*(((np.linspace(0, 1, 256)-0.5)*2)**3/2+0.5)\n",
    "\n",
    "plt.plot(sclae)\n",
    "\n",
    "bwr_nc = bwr_cmap(sclae)\n",
    "bwr_nc = ListedColormap(bwr_nc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2D plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "TTilde_gird=np.array(df_all.TTilde.tolist())\n",
    "Time_grid=np.linspace(0,300,151)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "TTilde_gird_edge=np.zeros(len(TTilde_gird)+1)\n",
    "Time_grid_edge=np.zeros(len(Time_grid)+1)\n",
    "\n",
    "TTilde_gird_edge[0]=TTilde_gird[0]-(TTilde_gird[1]-TTilde_gird[0])/2\n",
    "TTilde_gird_edge[-1]=TTilde_gird[-1]+(TTilde_gird[-1]-TTilde_gird[-2])/2\n",
    "\n",
    "for i in range(len(TTilde_gird_edge)-2):\n",
    "    TTilde_gird_edge[i+1]=(TTilde_gird[i]+TTilde_gird[i+1])/2\n",
    "    \n",
    "    \n",
    "Time_grid_edge[0]=Time_grid[0]-(Time_grid[1]-Time_grid[0])/2\n",
    "Time_grid_edge[-1]=Time_grid[-1]+(Time_grid[-1]-Time_grid[-2])/2\n",
    "\n",
    "for i in range(len(Time_grid_edge)-2):\n",
    "    Time_grid_edge[i+1]=(Time_grid[i]+Time_grid[i+1])/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalized rf response\n",
    "C_LH=np.zeros([len(Time_grid),len(TTilde_gird)])\n",
    "\n",
    "for i in range(len(TTilde_gird)):\n",
    "    r=df_all.iloc[i]\n",
    "    \n",
    "    Time_temp=df_all.iloc[i].WaitTime\n",
    "    RF_temp=df_all.iloc[i].RF_evolutionNN\n",
    "\n",
    "    \n",
    "    for j in range(len(Time_grid)):\n",
    "        ind=np.argmin(np.abs(Time_temp-Time_grid[j]))\n",
    "        C_LH[j,i]=RF_temp[ind]\n",
    "        \n",
    "        if np.abs(Time_temp[ind]-Time_grid[j])>10:\n",
    "            C_LH[j,i]=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### supersample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "TTildeSS_grid_edge=np.linspace(0.1,0.22,391)\n",
    "TTildeSS_grid=(TTildeSS_grid_edge[:-1]+TTildeSS_grid_edge[1:])/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "C_LH_SS=np.zeros([len(Time_grid),len(TTildeSS_grid)])\n",
    "weight=C_LH_SS*0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,t in enumerate(TTildeSS_grid):\n",
    "    \n",
    "    if t<= np.min(TTilde_gird_edge):\n",
    "        ind_T=0\n",
    "    elif t>=np.max(TTilde_gird_edge):\n",
    "        ind_T=len(TTilde_gird)-1\n",
    "    else:\n",
    "        ind_T=np.array(range(len(TTilde_gird_edge)))[TTilde_gird_edge>t][0]-1\n",
    "        \n",
    "#     print(ind_T)\n",
    "    C_LH_SS[:,i]=C_LH[:,ind_T]\n",
    "#     weight[:,i]=1/(TTilde_gird_edge[ind_T+1]-TTilde_gird_edge[ind_T])+C_LH[:,ind_T]*0\n",
    "    weight[:,i]=1++C_LH[:,ind_T]*0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(151, 390)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C_LH_SS.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Gaussian Average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gausian average\n",
    "\n",
    "C_LH_GA=C_LH_SS*0\n",
    "\n",
    "TTilde_SS_mesh,Time_grid_mesh=np.meshgrid(TTildeSS_grid,Time_grid)\n",
    "\n",
    "Delta_TTilde=TTildeSS_grid[1]-TTildeSS_grid[0]\n",
    "Delta_t=5\n",
    "Delta_t_2=12\n",
    "\n",
    "for i,T_temp in enumerate(TTildeSS_grid):\n",
    "    for j,t_temp in enumerate(Time_grid):\n",
    "        \n",
    "        if TTildeSS_grid[i]<=0.163:\n",
    "            multiplier=np.exp(-((TTilde_SS_mesh-T_temp)/Delta_TTilde)**2-((Time_grid_mesh-t_temp)/Delta_t)**2)\n",
    "        else:\n",
    "            multiplier=np.exp(-((TTilde_SS_mesh-T_temp)/Delta_TTilde)**2-((Time_grid_mesh-t_temp)/Delta_t_2)**2)\n",
    "        C_LH_GA[j,i]=np.sum(C_LH_SS*multiplier*weight)/np.sum(multiplier*weight)\n",
    "    C_LH_GA[:,i]=C_LH_GA[:,i]/C_LH_GA[:,i][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 450x330 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(nrows=1,ncols=1,figsize=[4.5,3.3])\n",
    "\n",
    "ax.set_position([0.12,0.14,0.36,0.83])\n",
    "\n",
    "TTilde_gird_edge[0]=0.1\n",
    "plt.pcolormesh(Time_grid_edge,TTildeSS_grid_edge/Tc,C_LH_GA.T,cmap=bwr_nc)\n",
    "\n",
    "\n",
    "plt.clim(-1,1)\n",
    "plt.xlim([0,150])\n",
    "plt.ylim([0.6,1.25])\n",
    "# plt.gca().invert_yaxis()\n",
    "\n",
    "ax.tick_params(axis='x',direction='out',length=1.5,top=0,right=0,labelsize=9)\n",
    "ax.tick_params(axis='y',direction='out',length=1.5,top=0,right=0,labelsize=9,pad=0.5)\n",
    "ax.set_xlabel('t (ms)',fontsize=9,labelpad=1)\n",
    "plt.ylabel(r'$T/T_c$',fontsize=9,labelpad=2)\n",
    "\n",
    "\n",
    "ax_cb=fig.add_axes([0.12,0.975,0.36,0.01])\n",
    "ax_cb.tick_params(axis='both',direction='in',right=1,labelsize=7,left=0,length=2)\n",
    "cb=plt.colorbar(cax=ax_cb,orientation='horizontal')\n",
    "cb.set_ticks([-1,1])\n",
    "ax_cb.xaxis.tick_top()\n",
    "ax_cb.set_xlabel('$\\Delta T$ (a.u.)',fontsize=7,labelpad=-5)\n",
    "ax_cb.xaxis.set_label_position('top')\n",
    "fig.savefig('Fig2E.pdf',dpi=300)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# i=14\n",
    "# print('T/Tc={:.2f}'.format(df_all.TTilde[i]/Tc))\n",
    "\n",
    "pd.DataFrame(C_LH_SS.T).to_clipboard(index=False,header=False)\n",
    "# pd.DataFrame(df_all.RF_evolutionN[i]).to_clipboard(index=False,header=False)\n",
    "# df_all.rf_errN[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1770817638314544"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C_LH_SS[0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {
    "height": "120px",
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   "number_sections": true,
   "sideBar": true,
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   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "630px",
    "left": "0px",
    "right": "1112px",
    "top": "106px",
    "width": "212px"
   },
   "toc_section_display": "block",
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